Integrating single-cell and bulk sequencing data to identify glycosylation-based genes in non-alcoholic fatty liver disease-associated hepatocellular carcinoma

Zhijia Zhou; Yanan Gao; Longxin Deng; Xiaole Lu; Yancheng Lai; Jieke Wu; Shaodong Chen; Chengzhong Li; Huiqing Liang

doi:10.7717/peerj.17002

Integrating single-cell and bulk sequencing data to identify glycosylation-based genes in non-alcoholic fatty liver disease-associated hepatocellular carcinoma

PeerJ. 2024 Mar 18:12:e17002. doi: 10.7717/peerj.17002. eCollection 2024.

Authors

Zhijia Zhou^#¹, Yanan Gao^#², Longxin Deng², Xiaole Lu², Yancheng Lai², Jieke Wu², Shaodong Chen³, Chengzhong Li⁴, Huiqing Liang^{5

6}

Affiliations

¹ Department of Hepatology, ShuGuang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.
² The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong Province, China.
³ Xiamen University, Xiamen, Fujian Province, China.
⁴ Changhai Hospital, The Second Military Medical University, Shanghai, China.
⁵ Hepatology Unit, Xiamen Hospital of Traditional Chinese Medicine, Xiamen, Fujian Province, China.
⁶ College of Traditional Chinese Medicine, Beijing University of Traditional Chinese Medicine, Beijing, China.

^# Contributed equally.

Abstract

Background: The incidence of non-alcoholic fatty liver disease (NAFLD) associated hepatocellular carcinoma (HCC) has been increasing. However, the role of glycosylation, an important modification that alters cellular differentiation and immune regulation, in the progression of NAFLD to HCC is rare.

Methods: We used the NAFLD-HCC single-cell dataset to identify variation in the expression of glycosylation patterns between different cells and used the HCC bulk dataset to establish a link between these variations and the prognosis of HCC patients. Then, machine learning algorithms were used to identify those glycosylation-related signatures with prognostic significance and to construct a model for predicting the prognosis of HCC patients. Moreover, it was validated in high-fat diet-induced mice and clinical cohorts.

Results: The NAFLD-HCC Glycogene Risk Model (NHGRM) signature included the following genes: SPP1, SOCS2, SAPCD2, S100A9, RAMP3, and CSAD. The higher NHGRM scores were associated with a poorer prognosis, stronger immune-related features, immune cell infiltration and immunity scores. Animal experiments, external and clinical cohorts confirmed the expression of these genes.

Conclusion: The genetic signature we identified may serve as a potential indicator of survival in patients with NAFLD-HCC and provide new perspectives for elucidating the role of glycosylation-related signatures in this pathologic process.

Keywords: Glycosylation; Hepatocellular carcinoma; Immunotherapy; Machine learning; Nonalcoholic fatty liver disease.

MeSH terms

Animals
Carcinoma, Hepatocellular* / genetics
Glycosylation
Humans
Liver Neoplasms* / genetics
Mice
Non-alcoholic Fatty Liver Disease* / genetics
Nuclear Proteins / metabolism

Substances

SAPCD2 protein, human
Nuclear Proteins

Grants and funding

This work was supported by the National Science Natural Foundation of China (Nos. 82174141 and 82374353). The funders had a role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.